Comparison of satellite-derived land surface temperature and air temperature from meteorological stations on the Pan-Arctic scale

Satellite-based temperature measurements are an important indicator for global climate change studies over large areas. Records from Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Very High Resolution Radiometer (AVHRR) and (Advanced) Along Track Scanning Radiometer ((A)ATSR) are providing long-term time series information. Assessing the quality of remote sensing-based temperature measurements provides feedback to the climate modeling community and other users by identifying agreements and discrepancies when compared to temperature records from meteorological stations. This paper presents a comparison of state-of-the-art remote sensing-based land surface temperature data with air temperature measurements from meteorological stations on a pan-arctic scale (north of 60° latitude). Within this study, we compared land surface temperature products from (A)ATSR, MODIS and AVHRR with an in situ air temperature (Tair) database provided by the National Climate Data Center (NCDC). Despite analyzing the whole acquisition time period of each land surface temperature product, we focused on the inter-annual variability comparing land surface temperature (LST) and air temperature for the overlapping time period of the remote sensing data (2000–2005). In addition, land cover information was included in the evaluation approach by using GLC2000. MODIS has been identified as having the highest agreement in comparison to air temperature records. The time series of (A)ATSR is highly variable, whereas inconsistencies in land surface temperature data from AVHRR have been found

Operational Forest Monitoring in Siberia Using Multi-source Earth Observation Data

Forest cover disturbance rates are increasing in the forests of Siberia due to intensification of human activities and climate change. In this paper two satellite data sources were used for automated forest cover change detection. Annual ALOS PALSAR backscatter mosaics (2007–2010) were used for yearly forest loss monitoring. Time series of the Enhanced Vegetation Index (EVI, 2000–2014) from the Moderate Resolution Imaging Spectroradiometer (MODIS) were integrated in a web-based data middleware system to assess the capabilities of a near-real time detection of forest disturbances using the break point detection by additive season and trends (Bfast) method. The SAR-based average accuracy of the forest loss detection was 70 %, whereas the MODIS-based change assessment using breakpoint detection achieved average accuracies of 50 % for trend-based breakpoints and 43.4 % for season-based breakpoints. It was demonstrated that SAR remote sensing is a highly accurate tool for up-to-date forest monitoring. Web-based data middleware systems like the Earth Observation Monitor, linked with MODIS time series, provide access and easy-to-use tools for on demand change monitoring in remote Siberian forests

Identification of land surface temperature and albedo trends in AVHRR Pathfinder data from 1982 to 2005 for northern Siberia

The arctic regions are highly vulnerable to climate change. Climate models predict an increase in global mean temperatures for the upcoming century. The arctic environment is subject to significant changes of the land surface. Especially the changes of vegetation pattern and the phenological cycle in the taiga–tundra transition area are of high importance in climate change research. This study focuses on time series and trend analysis of land surface temperature, albedo, snow water equivalent, and normalized difference vegetation index information in the time period of 1982–2005 for northern Siberia. The findings show strong dependencies between these parameters and their inter-annual dynamics, which indicate changes in vegetation growing period. We found a strong negative correlation between land surface temperature and albedo conditions for the beginning (60–90%) of the growing season for selected hot spot trend regions in northern Siberi